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Brazil’s Productivity Dynamics
Rong Qian, Jorge Thompson Araújo and Antonio Nucifora
February, 2018
I. Introduction
1. Recent developments in the theory and empirics of growth have emphasized the
interplay of macroeconomic and microeconomic factors in long-term development. Most
notably, the Schumpeterian growth paradigm 1 has highlighted the crucial role of creative
destruction, namely, the displacement of old technologies by new innovations, in promoting
sustained aggregate productivity growth in the long run (see Box 1 for a discussion on the concept
and measurement of productivity). In particular, the Schumpeterian approach is underpinned by
three core notions: (i) innovations are the primary reason for sustained productivity growth,
particularly for firms, sectors and countries closer to the technological frontier; (ii) innovations
are often driven by entrepreneurial initiative, which in turn responds to economic incentives that
can be positively or negatively affected by policies and institutions; and (iii) innovations can
translate into economic growth insofar as creative destruction—or the “competition process
between the old and the new”—can run its course more or less unimpeded.
2. This chapter shows that distortions have prevented the Brazilian economy from
benefiting fully from the Schumpeterian forces of innovation and creative destruction. In
order to so, it examines determinants of productivity growth in Brazil from aggregate, sectoral and
firm-level viewpoints. The chapter highlights an economy with significant dysfunctions, which are
at the root of the feeble performance in productivity. The chapter provides an analysis of Brazil’s
aggregate macro level trends in growth and structural transformation. It discusses the rate of
productivity growth and the extent of reallocation of resources toward the most productive sectors.
In particular, the analysis shows that the Brazilian economy is characterized by: (i) limited
structural change, with recent economic performance driven mainly by the increasing productivity
in agriculture and the expanding size of the services sector; (ii) the existence of widespread policy
distortions, notably in the financial sector and through the adoption of certain types of industrial
policies, which have contributed to low capital accumulation and to a suboptimal allocation of
resources, keeping economic performance below potential; (iii) dysfunctional firm-level dynamics
where low productivity firms are able to survive indefinitely, possibly because of a policy
framework which undermines competition and results in stagnant productivity, weak jobs creation,
and feeble export performance. These findings shed light on an economy in which barriers to the
workings of Schumpeterian creative destruction has resulted in businesses competing for
government privileges (rents) rather than investing in frontier technologies to be more competitive
in the market place, resulting in an weak rate of aggregate productivivty growth in the economy
as a whole.
1 Aghion, Akcigit and Howitt (2013) and Aghion and Bircan (2017) provide useful summaries of the latest
developments of the Schumpeterian growth paradigm.
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3. The chapter is organized as follows. Section II outlines the main trends in productivity
at the aggregate level and examines the role of structural change in overall productivity growth,
introducing the notion of factor misallocation that will permeate much of the discussion in this
chapter. Section III looks into the firm-level evidence and sheds light on the productivity dynamics
of Brazilian firms. Section IV concludes.
Box 1. Understanding and Measuring Productivity
What is productivity and why is it important?
Productivity is an indicator of technical efficiency that captures how firms, industries (groups of firms
in the same product market), sectors (groups of industries) or the country transform measured inputs into
the production of goods and services. The two most common productivity measures are: labor
productivity (LP) and total factor productivity (TFP). While these two concepts are related, they are in
fact distinct.
LP captures the value of the outputs produced (or value-added) divided by the number of workers. It is
therefore a measure of the amount of wealth creation per worker. This is determined by the amount of
capital and other non-labor inputs available to workers, as well as the efficiency with which these inputs
are used. Nobel prize winning economist Paul Krugman famously said: “Productivity isn't everything,
but in the long run it is almost everything. A country’s ability to improve its standard of living over time
depends almost entirely on its ability to raise its output per worker.” (Krugman 1994). Hence the
evolution of labor productivity is key to understand the growth in living standards over time.
TFP is derived as a residual of output once the impact of all measured inputs is accounted for, notably
labor (augmented by the quality of human capital) and capital (including physical capital, such as
machinery, computers and buildings, and also energy, materials, and services, plus some measures of
intangible capital). TFP therefore captures the efficiency with which all inputs are combined into the
productive process. The evolution of TFP can be thought of as an economic measure of technical
progress. TFP growth can be the result of reallocation (shifting resources from less to more efficient
firms within the same industries or across industries and sectors) or innovation. Innovation can include
the development of entirely new products and technologies and the adoption and adaptation of existing
technologies and processes.(*) In the long-term, how efficiently countries use all available inputs is the
key determinant of differences in economic growth rates and resulting income levels.(**)
Although the two measures of productivity are generally highly correlated, since efficiency gains
obtained through TFP will make labor more productive, they will not be correlated if LP gains are
obtained through the accumulation of capital instead of TFP.
Accurately measuring productivity is a challenging task. For instance, deriving TFP as a residual from a
production function will yield accurate estimates only if output and factor prices adequately reflect
marginal costs and marginal factor productivity. If this is not the case, the effect of price distortions due
to market power or government intervention may be misinterpreted as changes in efficiency. Another
common difficulty is associated with measuring quality of output and factor inputs accurately. These
measurement issues need to be borne in mind in the discussion that follows.
Notes: (*) See Haskel et al. (2011) and Cirera and Maloney (2017). The benefits from innovation and resulting productivity
growth go significantly beyond income: Lichtenberg (2014) documents a large impact of foreign drug research on
life expectancy. Hence, the ability to transfer and adapt health advances developed elsewhere with modest amounts
of innovative effort likely generate large social returns for adopting countries. (**) Cross-country evidence confirms that TFP is the main driver of income differences across countries and of
sustainable per capita income growth (Easterly and Levine, 2001; Caselli, 2016).
3
II. Aggregate Trends in Productivity
Growth between 1996 and 2015 has mainly been driven by labor accumulation
4. The Brazilian economy benefitted from a prolonged – but ultimately unsustainable –
period of very high growth which ended with the “lost decade” of the 1980s. Between 1950
and 1980—which Bacha and Bonelli (2015) call the Post WW-II Golden Age—Brazil’s average
GDP growth rate averaged an impressive 7.4 percent per year. Growth was volatile since 1961,
however, with a downward trend from 1974 to 1981, and was followed by a decade of stagnation.
During the “lost decade” (1981-1992), in the wake of the debt crisis, Brazil’s GDP growth rate
averaged a paltry 1.4 percent per year and the economy virtually stopped catching up with the
developed world.2
5. A period of faster growth started in the 1990s and reversed again after 2010. During
1993-2010, the GDP growth rate kept fluctuating, but basically maintained positive levels.
However, after 2010, the deterioration in both the external environment and domestic policies led
to a steady decline in growth (Figure 1). Growth declined from an average of 4.5 percent per year
in 2006-2010 to 2.4 percent in 2011-2014, followed by contractions of 3.8 percent and 3.6 percent
in 2015 and 2016, the deepest recession in Brazil’s history.
6. Economic growth since the mid-1990s has been driven largely by labor accumulation,
namely an expansion of the employed labor force and its quality. An analysis of the
decomposition of GDP growth highlights that Brazil’s growth over the past two decades was
largely driven by factor accumulation, with only a small contribution from improvements in Total
Factor Productivity (TFP).3 During the 1996-2015 period, 39 percent of growth was driven by
capital accumulation (Figures 2 and 3, and Table 1). The contribution of capital decreased from
31 percent during 1996-2003 to just 19 percent during 2003-2009. It subsequently accounted for
88 percent of the (meager) growth from 2010. “Labor accumulation”, namely the increase in the
labor force and its quality (human capital) jointly accounted for 64 percent of growth during the
1996-2015 period. Labor accumulation has been decreasing over time, from 98 percent of growth
in the 1996-2003 period, down to 43 percent in the post 2010 period. As discussed below, these
patterns may reflect the combination of the gradual reduction in the contribution of labor and its
2 This pattern can be seen throughout Latin America. Ferreira and et. (2011) point out that mean (median) TFP for
Latin America relative to the US averaged 82% (79%) between 1960 and 1980, plummeting to 54% (60%) in 2007. 3 Total Factor Productivity (TFP) is a commonly used measure of productivity. In a nutshell TFP is calculated as the
residual growth that cannot be attributed to increased use of labor and capital. In other words, everything not
captured by changes in labor or capital is picked up by TFP growth. This includes measurement errors and changes
in utilization rates of factor inputs. This growth accounting analysis is based on a simple production function that
allow to assess the sources of growth since 1996. It uses a constant return to scale Cobb-Douglas production
function with a share of capital of 46 percent of GDP. The labor supply is estimated adjusting the working age
population by participation rate, employment rate and average year of education. The stock of capital was estimated
using the Perpetual Inventory Method assuming a depreciation rate of 5 percent. It should be noted that estimating
the capital stock is beset with problems. It is also worth noting that Total Factor Productivity can be shown to be a
component of labor productivity (which we will discuss below), but that the two do not coincide as the latter is also
influenced by the amount of capital per worker.
4
quality, as well as a strong government effort to increase credit and investment into some sectors
of the economy through public banks since 2007, and especially after 2011.
7. Total factor productivity has been a drag on growth (i.e. a negative contribution) on
average over the 1996-2015 period. The contribution of TFP to economy-wide growth has moved
from negative 29 percent during 1996-2003 to positive 39 percent during 2003-2009, to negative
57 percent during 2009-2015 (Figure 3). This largely reflects our calculation of TFP as a residual
(in line with standard growth accounting methodologies) such that the recent period of growth
slowdown and recession appears caused by a negative contribution of productivity, when in fact it
was driven by other unobserved factors which are bundled into the TFP residual, such as for
instance an increase in spare capacity and inventories. Nevertheless, the results of this analysis
show that over time the Brazilian economy has not grown as a result of improvements in
productivity. The results also show that labor’s contribution has been declining steadily.
Figure 1. Downward trend in growth, particularly during 1974-1981 and after 2010
Figure 2. TFP has been volatile, but did not contribute to GDP growth on average over the
entire period
Source: WDI Source: IBGE, world bank staff calculation
-5
0
5
10
15
19
61
19
64
19
67
19
70
19
73
19
76
19
79
19
82
19
85
19
88
19
91
19
94
19
97
20
00
20
03
20
06
20
09
20
12
20
15
(real GDP growth and its trend, percentage points)
GDP growth rate Trend, HP filter
-5
-3
-1
1
3
5
7
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
(real GDP growth and contributions, percentage points)
Total Factor ProductivityCapitalLaborHuman CapitalReal GDP
5
Figure 3. TFP only contributed to Brazil’s growth performance during 2003-2009
Figure 4. TFP contribution to growth was above LAC and OECD averages but well below Asian
countries’
Source: IBGE, world bank staff calculation Source: IBGE, WDI, world bank staff calculation
Note: countries included in OECD: OECD members; in
LAC: Argentina, Chile, Colombia, Mexico and Peru; in
East Asia: Indonesia, Malaysia, Thailand, and
Philippines.
Table 1: Percentage Contributions to GDP Growth, 1996-2015
1996-2015 1996-2003 2003-2009 2009-2015
Capital Stock 39 31 19 88
Labor 38 68 22 44
Human Capital per Labor 24 30 20 25
Total Factor Productivity -1 -29 39 -57
Real GDP 2.6 2.2 4.2 1.7
8. International comparisons confirm that Brazil’s growth has been heavily dependent
on labor accumulation. The results of the growth accounting are affected by various broader
economic fluctuations such as the East Asia crisis in the late 1990s, the global financial crisis in
2008, and the ongoing recession in Brazil. Nevertheless, a few clear findings emerge. Capital
accumulation has played a greater role in all comparator countries, whether looking at the OECD
average, the LAC average, China and India (Figure 4). The contribution of labor has been very
large in Brazil, far above any other comparator. The improvement in the quality of human capital
was broadly in line with the rest of the LAC region and East Asia, but much larger than experienced
in China and India where, as mentioned, capital has played the driving role, and also productivity
growth.
1.50.9 0.8
0.70.8
0.4
0.70.8
1.5
-0.6
1.6
-1.0
-1.0
1.0
3.0
5.0
1996-2003 2003-2009 2009-2015
(real GDP growth and contributions, percentage points)
Total Factor Productivity Capital
Human Capital per Labor Labor
Real GDP
-25
-15
-5
5
15
25
35
45
55
65
19
96-2
01
5
19
96-2
01
3
19
96-2
01
3
19
96-2
01
3
19
96-2
01
3
19
96-2
01
3
Brazil OECD avg LAC East AsiaAvg
China India
(contribution to growth, percent)
Capital Stock LaborHuman Capital per Labor Total Factor Productivity
6
Figure 5. Value added per worker grew less than from other emerging economies over the period and decreased in recent years
Source: WDI
Note: Chile and Uruguay are excluded from the LAC group (as they are high income countries).
9. Low TFP is reflected in Brazil’s labor productivity, which grew less than advanced
countries and other emerging countries over the past 20 years. We calculate value added per
worker as a proxy for labor productivity. Between 1995 and 2015, value added per worker in Brazil
has been growing at about the same rate as the rest of Latin America and the Caribbean (LAC)
countries, but much below advanced economies and other emerging economies (Figure 5). It
declined briefly in 2009 as in the rest of the world. However, since 2014, while the rest of emerging
economies and advanced economies continued the upward trend in value added per worker, the
trend in Brazil and the rest of LAC reversed, with Brazil declining at a faster rate than LAC. Over
this entire period, the gap in labor productivity between Brazil (and LAC) and advanced countries
and other emerging countries continued to increase steadily. This contrasts with other emerging
countries, whose labor productivity has been rising faster than advanced economies notably since
2004, thereby moving to gradually close their productivity gap.
10. Overall, these results highlight that productivity has contributed little to growth in
Brazil, and that growth has been driven by demographics and higher employment levels.
Although potential gains from increasing capital stock and human capital are significant, compared
to other countries Brazil would benefit the most from increasing its TFP (Box 2). Before looking
at the determinants of productivity at the firm level, it is useful to briefly explore possible
characteristics of the Brazilian economy which could give rise to this pattern of productivity.
90
100
110
120
130
140
150
160
1995 2000 2005 2010 2015
Valued added per worker, 1995=100
Brazil LAC (excluding high income) Emerging Economies Advanced
7
Box 2: Brazil would benefit largely from increasing TFP, almost tripling its GDP per worker if Brazil achieves the US TFP level
Within LAC, Brazil would benefit the
most from increasing its TFP. Brazil’s
output per worker is only 19 percent that
of the United States, the lowest among
main LAC economies. At the same time,
Brazil does not have notably lower
capital stock per worker, or notably
lower education levels than its LAC
comparators. The reason for a much
lower output per worker is the low level
of TFP. Brazil would increase its income
per capita by 2.7 times if its TFP were as
high as that in the United States against
an increase of around two times for the
average LAC country. It is notable that countries like Chile and Mexico, who have gone through a process
of deep international integration and have largely open markets, show the lowest productivity gap to the
US. One of the messages of this report is that by following suit with more open markets, Brazil would be
able to raise substantially its own productivity level and re-start economic convergence even without major
increases in investment.
Source: Caselli (2016) and author’ calculations.
Note: Counterfactual refers to output per worker that countries would have had if their TFP were equal to that of
the US
Limited structural change and weak productivity growth
11. The growth accounting exercise highlights a lack of improvement in Total Factor
Productivity. Higher productivity growth is important because it implies greater wealth creation
per capita, which results in more jobs creation and the creation of better quality jobs. An economic
growth strategy entailing large factor accumulation is appropriate when a country has a large stock
of untapped human resources, such as is the case in demographically young countries with high
unemployment. Productivity growth, however, is required to generate more wealth per capita and
ultimately faster and better jobs creation. The rate of TFP growth is a good indicator of the overall
efficiency of the economy—it measures the improvement in the use of these factor inputs.
Increases in TFP (that is, efficiency improvements in the use of factor inputs) can take place within
a given production activity or sector (through within-firm improvements as well as through
reallocation of resources across firms within sectors), or can be the result of a reallocation of
resources across sectors. Therefore low TFP growth suggests the existence of barriers that prevent
a reallocation of resources toward more productive activities and/or innovation (better uses of
resources wherever they are located), thereby hampering the economy’s capacity to generate
wealth and jobs.
12. Brazil differences in productivity across sectors are in line with international
patterns. Persistent productivity gaps across firms within sectors are likely to be indicative of
misallocation of resources. Large productivity gaps across sectors suggest that reallocation of
workers from lower-productivity to higher-productivity sectors can be an important driver of
growth. In fact, in many high-growth countries, in particular in Asia, re-allocation of workers
19%24%
35%
23%
36%
53%47%
44%
54%58%
0%
10%
20%
30%
40%
50%
60%
70%
Brazil LAC Mexico Colombia Chile
Actual Counterfactual
8
across sectors has contributed positively to growth during the last twenty years (McMillan and
Rodrik 2011). Labor productivity in extractives, one of Brazil’s most productive sectors, was 10.6
times higher than productivity in the agricultural sector, which had the lowest productivity in 2005
(Table 1). In comparison, this ratio was 12.7 in Turkey and 11.2 in Chile (McMillan and Rodrik
2011). The agriculture-manufacturing gap in Brazil is also in line with other countries. In 2005,
labor productivity in manufacturing in Brazil was 4.1 times higher than in agriculture—this is
above the 2.8 in Latin America and 3.9 in Asia (McMillan and Rodrik 2011). What is noteworthy,
however, is the low productivity of the retail sector, which was only 1.4 times more productive
than agriculture in 2005 (and the same as agriculture by 2013). Box 3 offers additional detail on
the evolution of sectoral value-added per worker.
Table 1: Productivity levels (VA per worker) and productivity gaps across sectors, 2000-2013
Year Agriculture Extractive
Utilities
Manufacturing
Construction
Wholesale & Retail
Transport & Communicatio
ns
Gov service
s
Other Activitie
s
2000 (level)
2484.0 27137.6
32635.8 12541.3 8315.4 4318.6 11706.5
14807.8 10804.2
(gap from agric.) 1.0 10.9 13.1 5.0 3.3 1.7 4.7 6.0 4.3
2005 (level)
2923.7 30919.6
31895.3 11880.3 7694.8 4063.4 11754.3
14964.9 10791.9
(gap from agric.) 1.0 10.6 10.9 4.1 2.6 1.4 4.0 5.1 3.7
2010 (level)
3999.4 33413.5
32786.8 11690.9 8368.3 4808.8 13078.6
14226.1 12806.8
(gap from agric.) 1.0 8.4 8.2 2.9 2.1 1.2 3.3 3.6 3.2
2013 (level)
5107.9 28344.2
32793.4 11509.1 8698.1 4992.8 13432.7
13636.4 13061.4
(gap from agric.) 1.0 5.5 6.4 2.3 1.7 1.0 2.6 2.7 2.6
13. Sectoral productivity gaps have been reducing sharply over time, suggesting the
presence of desirable structural change. The extractives-agriculture gaps decreased to 5.5
percent by 2013. Similarly, the agriculture-manufacturing gap decreased to 2.3 by 2013 (Table 1).
In both cases this was due to a combination of increasing VA in agriculture and relative stagnation
in VA in extractives or even a contraction in manufacturing. This reflects the fact that with the
notable exception of agriculture, sectoral productivity in Brazil has grown extremely little. Hence
the contribution of agriculture has been twofold. First, an increase in within-sector productivity,
and second shedding labor in what is still the lowest productivity sector towards other higher-
productivity-albeit-relatively-stagnant sectors. Since the middle of 2003, productivity has been
slowly increasing also in the services sector (notably wholesale and retail and other services),
which in parallel expanded its employment substantially. In a sense these findings capture the
dynamics of the problem with the Brazilian economy, which seems to be driven by productivity
increases in agriculture and an expansion in the size of (the mildly productive) services sector, but
has seen widespread stagnation (and in fact a drop) of productivity in manufacturing and in most
of the services sectors. Box 2 summarizes the main medium-term trends in sectoral VA per worker
in Brazil.
9
4 Rodrik (2016) highlights both economic and political concerns with premature deindustrialization. On the
economic side, since manufacturing exhibits unconditional convergence (as per Rodrik, 2013), a declining share of
the industrial sectror could signal a lower potential for overall income convergence with advanced economies. On
the political side, Rodrik argues that early deindustrialization may damage the sustainability of democratic regimes,
as manufacturing decline could reduce the discipline and coordination—and thus the bargaining power—of the labor
force.
Box 3. Trends in Sectoral Value-Added per Worker in Brazil
While sectoral shifts in employment have had a favorable impact on aggregate value-added per worker
(at least since 2005), it is instructive to examine how labor productivity has evolved in specific sectors.
This can shed light onto an apparent paradox: while agriculture displayed the fastest rates of
productivity growth, employment shifts away from the agricultural sector have overall been
“productivity-enhancing”. Based on the analysis in this section, as well as on the findings reported in
Lisboa and Mello (2017), the following sectoral value-added trends are worth summarizing:
• Agriculture. Starting from a low base, agricultural VA per worker has systematically grown at
relatively high rates since at least the early 1990s. In particular, between 2000 and 2013,
cumulative labor productivity growth in agriculture was 105.6%, while services grew 11.7% and
manufacturing shrunk by 5.5%. To a large extent, this period of sustained growth reflects
technological advances, such as labor-saving technical progress, as documented in Bustos et al
(2016). In fact, the nature of technological change in agriculture affects the overall productivity
in the economy. In particular, the positive impact of agricultural productivity growth on
manufacturing depends on labor-saving technological change in agriculture reallocates workers
towards the industrial sector, while land-augmenting technical progress can retard
industrialization. However, it is important to distinguish between labor productivity levels and
growth rates: Despite its fast and nearly continuous growth, the agricultural sector as a whole
continues to generate relatively low levels of VA per worker, thus dampening its impact on the
overall economy. In addition, there is a composition effect at play: The agricultural sector is also
shedding labor, which by construction means that VA per worker is going up, all else constant.
• Manufacturing. In contrast with agriculture, manufacturing has seen a continuous decline in its
overall VA per worker during the same period, which has contributed to concerns about
“premature deindustrialization”—the shrinking of the manufacturing sector at much lower
income levels than those at which developed countries started to deindustrialize—in Brazil.4 In
absolute terms, however, manufacturing is still much more productive (in the sense of having
higher VA per worker) than both agriculture and services in the aggregate.
• Services. Employment shifts towards the services sector have been “productivity-enhancing”
during the period considered. The evolution over time of VA per worker in the services sector
since the 1990s followed a U-shaped pattern, with the lowest levels of labor productivity being
registered between 2001 and 2006. This pattern is largely driven by the retail services subsector,
of which VA per worker plummeted from 1997 through 2003, following a V-shaped recovery in
the subsequent years. It is interesting to note that VA per worker for the financial intermediation
and insurance services subsector displayed a significant acceleration in its growth rate since
2003—which probably contributed to the “productivity-enhancing” shift of employment from
agriculture to services.
10
14. To assess how much structural transformation has contributed to Brazil’s growth in
the past, we carried out a different decomposition of growth in per capita Value Added. In
order to explore the dynamics of the Brazilian economy we decompose per capita Value Added
growth in the contribution of changes in the demographics, the level of employment and
participation in the labor force, and the level of productivity growth (Box 4 and Figure 6). The
latter can then be further divided into two additional components: changes in sector level
productivity (“within” component) and changes arising from a reallocation of labor between
sectors (“across” component), which measures the extent of structural change in the economy. In
turn the structural change component can be further decomposed into “between-static” and
“between-dynamic” components, where the former (between-static) measures whether workers
Box 4: Decomposing labor productivity measures at the firm level
Analysts are particularly interested in understanding what drives changes in productivity—which in
turn are the main engine of long-run economic growth, prosperity and poverty reduction. It is useful to
decompose changes in productivity into its sources or components, both at the aggregate/sectoral and
firm levels:
1) At the aggregate level, the sources of economy-wide productivity change can be decomposed as
follows:
• Between-sector change or structural: measures the contribution of changes in sectoral
employment shares between sectors to economy-wide productivity growth. It contributes
positively (negatively) if high-productivity sectors increase (decrease) their market share.
• Within-sector: measures the average contribution of productivity growth occurring within each
sector. It contributes positively (negatively) to economy-wide productivity if labor productivity
increases (decreases) within a sector.
2) Similarly, the sources of labor productivity change within a sector (aka within each industry), can
be decomposed into five subcomponents, which include the Schumpeterian notions of firm entry and
exit:
• Within-firm: measures the contribution of productivity growth within surviving firms. It
contributes positively (negatively) to sector-level productivity if labor productivity of surviving
firms increases (decreases).
• Between-firm: measures the contribution of changes in market share to sector-level
productivity through reallocation between surviving firms. It contributes positively (negatively)
to sector-level productivity if there are market share expansions (reductions) of high-
productivity firms or reductions (expansions) of low-productivity firms.
• Cross: measures the contribution of firm-size growth through reallocation between surviving
firms. It contributes positively (negatively) to sector-level productivity if fast-growth firms
expand (shrink) in size or slow-growth firms shrink (expand).
• Entry: measures the average difference between entering firms’ productivity and initial
productivity at the sector level. It contributes positively (negatively) to sector-level productivity
if entering firms have higher (lower) productivity than the initial sector average.
Exit: measures the average difference between initial productivity at the sector level and exiting
firms’ productivity. It contributes positively (negatively) to sector-level productivity if exiting
firms have lower (higher) productivity than the initial sector average.
11
move to above-average productivity level sectors whereas the latter (between-dynamic) measures
whether productivity growth is higher in sectors that expand in terms of employment shares.
15. The results confirm that Brazilian economy has been characterized by low
productivity and some structural change over the past decades. Per capita VA increased
significantly from 1996-2002 (0.6 percent) to 2003-2008 (2.8 percent), and dropped remarkably
in 2009-2014 (1.8 percent) (Figure 7). Between 1996 and 2014, demographic factors and
employment contributed more than labor productivity to increases in per capita value added (VA),
more than 60 percent. A Shapley decomposition of per capita VA growth between labor
productivity (VA per worker) and employed labor force highlights that changes in labor
productivity contributed negatively to per capita VA growth between 1997 and 2002, and only
turned positive after 2003. Throughout the period increases in employed labor force (as a result of
demographic growth and increases in employment rate and in labor force participation) played an
important role and maintained a positive contribution between 0.7 to 1.3 percentage points to
annual VA growth (Figure 7). During 1996 to 2002, the change in labor productivity (VA per
worker) was negative 0.4 percent, but turned positive to 1.5 percent for 2003-2008, and remained
above 1 percent after 2008.
16. While most of the productivity originated from within-sector productivity
improvements, a positive contribution came from sectoral shifts.5 The decomposition of VA
per worker in its “within” and “across” components highlights that between 1996 and 2014 the
contribution of structural change to economic growth has been positive although not very strong.6
While employment movements across sectors contributed to overall productivity growth (positive
between static), labor did not move to sectors that were becoming more productive (negative
between dynamic) (Figure 8). The negative between-dynamic effect for 1996-2002 is driven by a
decline in labor productivity in utilities while expanding employment share. Between 2003 and
2008, manufacturing absorbed labor while labor productivity decline resulting in negative
between-dynamic, although much smaller than previous period. Similarly, between 2009 and
2013, mining and government services expanded their labor share while labor productivity of these
sectors declined slightly.
5 It is interesting to note that previous analysis (McMillan and Rodrik 2011; Schiffbauer, Sahnoun and Araujo
(2016]) – based on data covering the 1990-2005 period – found a "productivity-reducing" pattern of structural
change for Brazil. 6 In line with Figure 6, aggregate labor productivity growth is decomposed into within-sector productivity change,
between-static and between-dynamic. The within-effect is positive (negative) when the weighted change in labor
productivity levels in sectors is positive (negative). The between-static measures the contribution of labor
reallocation across sectors, being positive (negative) when labor moves from less (more) to more (less) productive
sectors. The between-dynamic captures the joint effect of changes in employment shares and sectoral productivity
growth. In is positive (negative) if workers are moving to sectors that are experiencing positive (negative)
productivity growth. In other words, between-static measures whether workers move to above-average productivity
level sectors whereas between-dynamic measures whether productivity growth is higher in sectors that expand in
terms of employment shares.
12
Figure 6: GDP Growth Decomposition
Figure 7. Productivity contributed to growth in VA per capita only after 2003
Figure 8. A significant portion of the productivity contribution was the result of structural change
Source: IBGE, WDI, Groningen, world bank staff
calculations
Note: Reported per capital VA growth is annualized
growth rate between 1996-2014.
Source: IBGE, WDI, Groningen, world bank staff
calculations
Note: Reported growth of VA per worker is average growth
rate between 1996-2014. The methodology for this
decomposition is different from that used for Figure 28.
Per capita
VA growth
Change in employment rate
Change in output per worker
(Labor productivity)
Changes "within" sectors
Changes "across" sectors
(Structural change)
Change between-dynamic
Change between-static
Change in demographic
structure
Change in participation rate
-0.4
1.51.1
0.0
0.4
0.1
0.3
0.3
0.0
0.8
0.6
0.6
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1996-2002 2003-2008 2009-2014
Decomposition of growth of VA per capita(percent)
Change in Share of Working Age PopulationChange in Participation RateChange in Employment rateChange in ProductivityAnnual Growth per capita Value Added
-1.0
1.3 1.01.3
0.60.4
-0.5
0.0 -0.1
-2.0
0.0
2.0
4.0
-2.0
0.0
2.0
4.0
1996-2002 2003-2008 2009-2014
Decomposition of annual growth of labor productivity (percent, 1996-2014)
between-dynamicbetween-staticwithinaverage annual labor productivity growth
13
Figure 9. Overall, labor shifts contributed to aggregate productivity improvement
Figure 10. Structural change increased productivity 10 percent in 2014
Source: IBGE, WDI, Groningen, world bank staff
calculations
Source: IBGE, WDI, Groningen, world bank staff
calculations
Note: to calculate the counterfactual labor productivity, it
uses 1996 employment distribution and actual labor
productivity.
17. As mentioned above, labor productivity increased very little between 1996 and 2014,
at a rate of 0.6 percent per year, contributing roughly 39 percent to GDP growth per capita.
A share of this productivity growth took place “within” sectors, accounting for 18 percent of real
GDP growth per capita over the period (or 0.3 percent per year). Structural change, the reallocation
of labor from low-productivity to high-productivity sectors, contributed a further 25 percent to the
change in VA per capita between 1996 and 2014 (or 0.43 percent per year). For comparison,
Macmillan and Rodrik (2011) calculated that during 1990 to 2005 the “within” component in
China, Hong Kong SAR, India, Malaysia, Mauritius, Taiwan, China and Turkey ranged from 7.8
percent per year to 1.7 percent per year, while the structural change component accounted for
between 1.4 percent per year to 0.4 percent per year. They also found, however, that in many Latin
American and Sub-Saharan African countries “structural change” between 1990 and 2005 has been
negative, depressing economic growth (McMillan and Rodrik 2011).
18. A steady shift in employment from agriculture to services underpins the productivity
gains from structural change. Figure 9 shows changes in employment shares and the relative
productivity of sectors, measured as log of the ratio between sectoral productivity and average
productivity between 1996 and 2014. For positive gains to occur through structural change, sectors
would either be located in the top-right corner (e.g. other services) where labor shifts into relative
high-productivity sectors or the third quadrant (e.g. agriculture) where labor shifts out from low
productivity sectors. Similarly, computing the counterfactual aggregate productivity using 1996
employment distribution and actual labor productivity dynamics and labor force increases, shows
that aggregate productivity increased purely due to structural change by around 5 percent in 2000
and roughly 10 percent in 2014 (Figure 10). Structural change (inter-sectoral reallocation) was
contributed 0.7 percentage points per year to per capita VA growth between 1997 and 2002, and
0.4 percentages points per year after 2003 (Figure 14 and Figure 15).
Agriculture
ExtractiveUtilities
Manufacturing
Construction
Wholesale and retail
Trasport and communications
Gov services OtherServices
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
-15 -10 -5 0 5 10
seco
tr p
rod
uct
ivit
y /
aver
age
pro
du
ctiv
ity
chan
ge
Change in employment share (1996-2014)
(change in average employment share, and deviation from average value added per worker, ratios)
7000
7500
8000
8500
9000
9500
10000
10500Labor productivity: actual vs counterpactual
(nominal prices)
Aggregate productivity counterfactual
14
Figure 11. Employment shift from agriculture to services
Source: IBGE, WDI, Groningen, world bank staff
calculations
Figure 12. Within other services, business services
increased the most as share of total VA...
Figure 13. …while labor shifted out from domestic services to business services.
Source: IBGE, WDI, Groningen, world bank staff
calculations
Source: IBGE, WDI, Groningen, world bank staff
calculations
19. Employment shifted from agriculture to services while the share of labor in industry
remained stable. Between 1996 and 2014, total employment increased from 72 to 106 million
workers as a result of demographic change and rising labor market participation. Most of the new
labor force went to services while agriculture shedded labor. The employment share of services
increased from 56 percent to 66 percent while the shares of employment in agriculture declined
from 25 percent in 1996 to 13 percent in 2014 (Figure 11). Within services, the main expansion
was in business services, accompanied by a contraction in domestic services. Labor shifted out
24.6 20.013.4
12.810.5
11.3
6.36.7
8.7
18.718.2
18.4
4.05.3
5.8
8.910.0 10.8
23.9 28.3 30.7
0
10
20
30
40
50
60
70
80
90
100
1996 2003 2014
Employment by sector (percent)
Others services
Gov services
Transport andcommunicationsWholesale andretailConstruction
Manufacturing
Utilities
Extractive
Agriculture
0%
5%
2003 2014
(percent of total VA)
Maintenance and repair servicesAccommodation and food servicesBusiness servicesPrivate EducationPrivate HealthServices provided to families and associationsDomestic services
0%
2%
4%
6%
8%
2003 2014
(percent of total employment)
Maintenance and repair services
Accommodation and food services
Business services
Private Education
Private Health
Services provided to families and associations
Domestic services
15
from agriculture to mainly other services. The employment share of other services increased from
23.9 percent in 1996 to 28.3 percent in 2003 to 30.7 percent in 2014 (Figure 12). Within other
services employment shifted out from domestic services, 7.5 percent of total employment in 2003
to 6.2 percent in 2014 while business services employed more labor increasing from 3.9 percent
of total employment in 2003 to 5.8 percent in 2014. Business services also increased its share in
total VA, from 5.5 percent in 2003 to 6.8 percent in 2014 (Figure 13).
20. The services sector made the greatest contribution to growth in VA per capita.
Decomposing labor productivity changes at the sectoral level, enables us to identify within and
between sector variations. Changes in services labor productivity (services VA per worker) was
the most important contributor to increase in overall VA per capita. It improved from -1.4
percentage points per year between 1997 and 2002 to 0.8 percentage points per year between 2003
and 2008, and 0.6 percentage points per year between 2009 and 2014 (Figure 14). At a more
disaggregated level, the only negative “within” sector changes after 2002 were from manufacturing
and government services (Figure 15). This is confirmed when looking at labor productivity growth
by sectors. While agriculture increased steadily, although from a low level, manufacturing labor
productivity started a declining trend in 2009, and services labor productivity experienced a fast
growth between 2007-2010 (Figure 16-19). Within services, the largest improvements in within
sector productivity were in other services (and as shown below this is focused in financial services
and real estate, wholesale and retail, and transportation).
Figure 14. Services contributed largely to per capita VA growth...
Figure 15. ...which was driven by “other services”, mainly financial services and real estate
Source: IBGE, WDI, Groningen, world bank staff
calculations
Note: the methodology decomposes per capita VA
growth into changes in output per worker within sectors
and reallocation of workers across sectors. Reallocation is
positive when labor moves from less productive sectors
into more productive sectors.
Source: IBGE, WDI, Groningen, world bank staff
calculations
Note: the reallocation terms are different from Figure 13
because labor moves among more narrowly defined sectors.
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1997-2002 2003-2008 2009-2014
Decomposition of VA per worker growth (change in productivity by sector, percentage points)
Services etc.
Industry
Agriculture
Growth in VA per worker
Intersectoral Reallocation Effect
-2
0
2
1997-2002 2003-2008 2009-2014
Decomposition of VA per worker growth(change in productivity by sector, percentage
points)
Other servicesGov servicesTransport & CommunicationsWholesale & RetailConstructionManufacturingUtilitiesMiningAgricultureChange in VA per workerIntersectoral Reallocation Effect
16
Figure 16. Agriculture labor productivity increased steadily
Figure 17... while manufacturing labor productiviy started a downward trend in 2009
Source: IBGE, Bank staff calculation Source: IBGE, Bank staff calculation
Figure 18. Labor productivity in construction started to decline in 2014
Figure 19. Service labor productivity increased rapidly until 2011 when it plateud
Source: IBGE, Bank staff calculation Source: IBGE, Bank staff calculation
21. The Brazilian economy has been able to efficiently reallocate resources from low-
return agriculture to high-return services. The results also highlight, however, that apart from
the reallocation of resources from agriculture to services, most of the economy appears to have
remained fairly stagnant. A decomposition of the sectoral contributions to VA growth during
2000 to 2014 shows that the positive structural change was largely limited to agriculture. There
was almost no structural change in manufacturing (except for the machinery), and within sector
productivity was negative such that manufacturing actually contributed negatively to growth.
Some services sectors were characterized by rapid productivity growth, notably financial services
and real estate, but absorbed little or no additional labor. Most of the employment growth within
the services sectors was in business services and other services (and in the public sector) which
are the less dynamic services sector—the expansion of these sectors contributed (positive)
0
1000
2000
3000
4000
5000
6000
19
961
997
19
981
999
20
002
001
20
022
003
20
042
005
20
062
007
20
082
009
20
102
011
20
122
013
20
14
Labor productivity: Agriculture
8000
9000
10000
11000
12000
13000
14000
19
961
997
19
981
999
20
002
001
20
022
003
20
042
005
20
062
007
20
082
009
20
102
011
20
122
013
20
14
Labor productivity :manufacturing
6000
6500
7000
7500
8000
8500
9000
9500
10000
10500
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Labor productivity: Construction
35000
37000
39000
41000
43000
45000
47000
490001
996
19
971
998
19
992
000
20
012
002
20
032
004
20
052
006
20
072
008
20
092
010
20
112
012
20
132
014
Labor productivity: Services
17
structural change since they absorbed employment from agriculture, and the average productivity
of these sectors is higher than in agriculture. It is interesting to note that within sector productivity
growth was negative in these services sectors (i.e most of the services sectors actually experienced
a contraction in productivity). Important parts of the economy therefore appear not to be affected
by factor reallocation and productivity improvements.
Figure 20. Decomposition of sectoral contributions to VA growth, 1996-2014
Source: IBGE, WDI, Groningen, world bank staff calculations
Figure 21. Decomposition of sectoral contributions to VA growth, 2000 -2014
Source: IBGE, WDI, Groningen, world bank staff calculations
-40
-30
-20
-10
0
10
20
30
40
50
Sectoral contribution to VA growth, 1996-2014(percentage points)
Employment change contribution
Within sector contribution
Structural change
Total contribution
-40
-30
-20
-10
0
10
20
30
40
50
Agr
icu
ltu
re
Min
ing
Mac
hin
ery
and
eq
uip
men
t
Res
t o
f m
anu
fact
uri
ng
Uti
lilit
ies
Co
nst
ruct
ion
Trad
e
Tran
spo
rt
Info
rmat
ion
ser
vice
s
Fin
anci
al s
ervi
ces
Rea
l est
ate
Bu
sin
ess
serv
ices
Oth
er s
ervi
ces
Go
vern
men
t Se
rvic
es
Sectoral contribution to labor productivity growth, 2000-2014(percentage points)
Employment change contribution Within sector contribution Structural change Total contribution
18
22. In fact while structural change has made a contribution to increasing productivity in
the past, Brazil’s structural composition is not the main source of its productivity gap. A
recent study across 35 economic sectors shows that if Brazil had the same sectoral labor allocation
as the US, aggregate productivity would increase 68 percent. But if Brazil had the same sectoral
productivities as the US, aggregate productivity would increase 430 percent (Veloso et al., 2017).
Hence, low Brazilian productivity relative to the US is mainly due to low productivity “within”
most activities.7 The next subsection examines one possible cause of this low productivity: the
poor allocation of resources across firms.
23. Further, there is a question as to whether our measure of productivity is inflated by
the monopolistic profits in the transport and telecommunications sectors, and in the other
services (notably financial services). Our assessment of productivity performance would be even
lower if we consider that at the sector level productivity appears to have increased the most in
financial services and real estate (Figure 20). These sectors appear to be characterized by limited
competition, such that the increase in value added per worker is likely to reflect an increase in
rents enjoyed by these sectors, in a sense syphoning off wealth creation from the rest of the
economy. As discussed below, and as will be elaborated in Chapter 3, the financial sector is
characterized by distortive interventions and limited competition which appear to generate
significant rents.
III. Firm-Level Productivity Dynamics
24. Productivity patterns at the aggregate level are expected to be broadly reflected in
firm level dynamics. Productivity may grow by increasing output per worker within a firm or by
workers and capital moving from less efficient too more efficient firms, within sector and across
sectors—that is, the process of creative destruction underpinning structural change as envisaged
in the Schumpeterian growth paradigm.
Productivity growth at the firm level dominated by within firm productivity improvements
25. Firm-level analysis confirms that productivity growth in Brazil has been hampered
by a combination of limited “within” firms labor productivity improvements and limited
reallocation of resources across firms. Firm-level data for the period 1987-2009 across 20
industries (of which 11 are manufacturing and the remaining services industries) allow such a
decomposition to be made for Brazil. By decomposing labor productivity growth into growth from
within-firms and those coming from various reallocation channels, Bazzi et al (2014) show that
the within-sector component explains most of the evolution in labor productivity over the period.
On average over 1991 through 2005, structural change accounted for about 18 percent of the
improvement in labor productivity, while within-sector labor productivity accounts for the balance
of 82 percent (Figure 22).8 The firm level results therefore suggest that the contribution of
7 Veloso et al. (2017) 8 Within-sector change measures the average sectoral productivity growth contribution to aggregate productivity
change while structural change measures the contribution of changes in sectoral employment shares to economy
wide productivity. Thus, an increase in the market share of sectors with relatively high productivity in the final year
results in rising aggregate productivity.
19
structural change is even smaller than what is observed at the aggregate level, although overall
labor productivity growth estimated with firm level data also remains very modest. Within sectors,
the within-firm component greatly dominates the cross-firm component over this period across all
firms (Figure 2.11). There are limited immediate gains from entry and exit, with both entering and
exiting firms exhibiting lower than average productivity.9 The patterns of entry, exit and firm
growth differ across size categories, however. Small firms with fast productivity growth seem to
attract more employment, potentially related to the shift of workers from informal to formal
employment, particularly in construction, retail and transportation (Barbosa Filho and Veloso,
2016). Among large firms, by contrast, labor appears to shift to less productive firms over time
(Bazzi et al., 2014). The latter pattern in particular indicates the presence of distortions in the
allocation of resources to the detriment of overall productivity growth
Figure 22. Within sector labor productivity
changes dominated overall changes, 1991-2005
Figure 23. Entrant firms reduce labor
productivity growth while exit contributes positively within all industries, 1991-2005
Source: Bazzi, Muendler and Ricky (2014)
Note: All sectors (20 industries of which 11 industries
within manufacturing).
Source: Bazzi, Muendler and Ricky (2014)
Dynamic entry of small firms contributes to slow productivity growth
26. Despite cost of opening and closing firms, entrepreneuship drive is strong in Brazil.
Brazil firms entry rates are above many LAC and other emerging countries (Figure 25). This
suggests that while the country’s environment to open up new firms is not especially restrictive,
there are barriers which do not allow for a more dynamic environment (thus hampering the process
9 The fact that new entrants have lower productivity is not that surprising. The critical point about the role of entry in
a well-functioning high-productivity growth economy is the contribution of relatively large numbers of new successful
firms to growth thanks to their rapid expansion after their initial adjustment to prevailing demand patterns. For the
US, Haltiwanger et al. (2013) find that high-growth firms tend to be relatively young and make disproportionate
contributions to output, productivity and employment growth. For Brazil, Bastos and Silva (2017) find that less than
1 percent of new entrant firms account over a third of the relatively low number of new jobs created by the cohort 13
years after birth.
20
of creative destruction which would lead to faster productivity growth). These entry rates (of
limited liability companies) may not, however, be good proxies for overall entry rates in the
economy.10
27. Entrant firms tend to reduce labor productivity while exiting firms contribute
positively to overall labor productivity growth. To look into more details on within-sector
sources of productivity change, when decomposing the within-sector part into five terms: within-
firm, between-firms, cross, entry, and exit, within-firm changes still dominate the overall changes,
while entry and exit effects also play important roles (Bazzi et al., 2014). Between 1987 and 2009,
entrant firms reduce labor productivity growth while exits contribute positively within all
industries of the economy (Figure 23). This entry-exit turnover suggests that although low
productivity firms exit, entering firms also have lower productivity than the exiting sector average.
In addition, in some years there was a reallocation of employment from high to low labor
productivity firms, indicated by the negative changes of between terms.
Figure 24. Negative contribution to labor
productivity of new firms is larger in small firms.
However, among small firms more productive
firms expand faster.
Figure 25. Firm Entry Rates in Various
Countries, 2004-2009
Source: Bazzi, Muendler and Ricky (2014) Source: Klapper and Love (2010)
Note: Entry density measures the number of newly
registered limited liability firms per 1,000 working-age
people (ages 15-64).
28. The negative effect on labor productivity from entrant firms is stronger in small
firms, although productive small firms tend to expand faster. When firms are split into below
and above 30 employees, the productivity impact of entrant firms is mostly negative among small
firms, meaning entering firms have lower productivity than the initial sector average driving down
sector-level productivity, while this is not the case for larger firms (Figure 24). However, between-
firm effects are mostly positive and larger among smaller firms, which suggests that more
productive small firms expand faster. On the other hand, among large firms, the between-firm term
10 Reliable cross-country data on entry rates are difficult to come by. In interpreting the figure it is important to bear
in mind that limited liability companies comprise only a subset of all firms, and the numbers may thus not be
representative of the private sector at large
00.5
11.5
22.5
33.5
44.5
5N
orw
ay
Ru
ssia
Fran
ce
Cro
atia
Mal
aysi
a
Cze
ch R
epu
blic
Ch
ile
Per
u
Bra
zil
BR
ICS
East
Asi
a
LAC
Co
lom
bia
Sou
th A
fric
a
Turk
ey
Thai
lan
d
Mex
ico
Arg
enti
na
Ph
ilip
pin
es
Ind
on
esi
a
Ind
ia
Entry Density Limited Liability Companies (2004-2009)
21
is negative on average across years, indicating the expansion of market share of less productive
firms.
29. Most formal firm entry is by micro firms, which grow fast the second year and grow
slowly thereafter, or immediately die in their second year. The average number of employees
of plants in their first year is 2.4. Firms experience a high growth rate in second year when
mortality rate is also the highest (Figure 26 and Figure 27). After the second year, size of the
survived firms continues to increase, but at decreasing rates. Although typical establishment born
in the Brazilian formal sector grows almost fivefold in the first 12 years in the firm size, small
firms rarely ever become medium or large size firms (Figure 28). In contrast, surviving large firms
expand at a steadily rate from year 5 onward.
Figure 26. Brazilian formal firms are typically born small, grow fast the second year and then grow slowly thereafter
Figure 27. Mortality rate of firms is also highest the second year
Source: Coelho, Corseuil, and Foguel (2016) Source: Coelho, Corseuil, and Foguel (2016)
Figure 28. Smaller firms at birth grow slower than larger firms
Source: Coelho, Corseuil, and Foguel (2016)
2.4
5.3
6.77.5
8.28.7
9.39.8
10.410.9
11.412.0
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12
Nu
mb
er o
f em
plo
yees
Establishment age
6.9%
12.8%
9.7%
8.2%7.3%
6.6%6.1%
5.7% 5.4% 5.2% 5.0%4.3%
0%
2%
4%
6%
8%
10%
12%
14%
1 2 3 4 5 6 7 8 9 10 11 12
Establishment age
1.3 3.2 4.2 4.9 5.4 5.9 6.4 6.8 7.3 7.7 8.2 8.6
17.8
32.436.1 38.2 40.1 42.3 44.6 46.6 49.3 51.8
54.8 56.6
156.9
221.6222.9223.4231.3
240.6249.3
259.2271.9
282.5288.8294.2
100
150
200
250
300
350
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12
Nu
mb
er o
f em
plo
yee
s
Company ageFirm size (0,9] Firm size (9,49]
Firm size (49,∞)
22
30. Beyond micro firms, firm dynamics and jobs creation in Brazil’s manufacturing and
agriculture sectors are characterized by extremely high firm survival rates. The permanence
of firms in the same size category is high—in other words firms’ growth is extremely
constrained—and large old firms dominate the market, based on firm entry, exit and employment
data from 2004 to 2013 (Silva, Maciente and Gukovas, 2015). Very large firms (with more than
1000 workers) consistently account for more than 35 percent of all jobs, even though they represent
only 0.16 percent of all firms. Most jobs are in older firms (aged 20+) (60 percent), although young
firms (aged 5-10) gained employment share vis a vis mature firms (aged 10-20) since 2007. The
construction and trade sector gained employment share while manufacturing and agriculture loss,
but no other major pattern of structural change occurred. Employment share of exporters
decreased, especially among small exporters. Employment shares of high, medium and low skill
firms remained fairly stable. “Old and large” and “new” (<1yr) firms account for most net job
creation and there is very few firm entry at large scale. Small and young firms had negative net
job creation, however, as these firms mostly die very young. The share on net job creation of very
large and old firms remains remarkably constant through time. The large (with more than 1000
workers) and old (30+yrs) and “high employment growth” (20%+) firms operate mostly in
construction and manufacturing (and are a total of 154 firms).
31. High entry rate driven by small firms contributes to slow productivity growth. An
international comparison of jobs creation by firm size and age further highlights the same
limitations with firm dynamics in Brazil. The pattern of new firm entry and jobs creation appears
focused on small firms in Brazil, while it is driven by medium and larger firms in other countries
(Figure 29). In Germany medium and large firms grow for several years, and some of the more
established firms also continue to grow. In Brazil the main source of jobs creation is from very
old and established firms. This is similar to Morocco where large firms continue to grow steadily
after entry—a pattern which is associated with strong political connections to the country’s
establishment however and not necessarily linked to firms’ productivity. The fact that most of new
firms are small, likely entered into market by necessity instead of real entrepreneurship, could be
contributing to overall low productivity growth as it drains resources to unproductive investment
and results in high exit rate of infant firms.
23
Figure 29. Net job creation by firm age and size in Brazil and selected countries (2013)
Brazil
Germany Czech Republic
Chile Morocco
Source Brazil: Silva, J., Maciente A., and Gukovas R. (2015). “Employment growth, skill use and labor productivity in Brazil.” World Bank, mimeo. Source Germany, Chile, Chech Republic and Morocco: Hallward-Driemeier and Reyes (2014).
24
Large TFP dispersion suggests presence of misallocation of resources
32. TFP dispersion is high and asymmetric in manufacturing with a fat tail on lower
TFP, suggesting the existence of barriers to exit and competition.level (Figure 30). There are
two facts that suggest the presence of misallocation of reources at the firm level. Firstly, the more
disperse TFP, the greater can be the misallocation. This is because high dispersion implies that
some firms are more able to produce output with the same amount of inputs. Secondly, the median
is greater than the average implies that TFP distribution is asymmetric. The distribution of the
logarithm of TFP in manufacturing sector for 1996, 2005 and 2011 shows that dispersion is
relatively high in each time period (Figure 30). Furthermore, the figure shows that the distribution
becomes more dispersed through time, with more firms dropping into the low TFP zone—that is
suggesting that the problem is increasing over time. It is reasonable to assume that this large
number of unproductive firms survive thanks to various uncompetitive practices, such as barriers
to competition from other firms, privileged access to government contracts, access to subsidized
credit, ad hoc tax exemptions and other financial incentives from public programs.
33. Although the dispersion level is comparable to other LAC countries, there is a larger
fat tail on lower TFP in Brazil relative to peers, again suggesting problems in the competitive
environment. An analysis of various measures of dispersion of total factor productivity (namely
(a) the difference between the 90th and the 10th percentile, (b) the inter-quartile range and (c) the
standard deviation, highlight that relative to other LAC countries), highlights that Brazil display
higher productivity dispersion when looking at the difference between productivity of the 90th and
the 10th percentile firms than the US, but lower than Colombia and at par with Mexico. However,
the inter-quartile range in Brazil is the lowest among comparators suggesting the presence of fat
tail on the lower TFP zone, consistent with the TFP dispersion (Figure 31).
Figure 30. TFP dispersion is high and asymmetric in manufacturing with a large mass in low TFP
Figure 31. But dispersion level is comparable to other LAC countries
Source: Vasconcelos (2017) Source: Busso, Madrigal and Pages (2013)
34. An international comparison of firms’ productivity distributions confirms a strong
concentration of low productivity companies and a lower concentration of firms with average
productivity in Brazil compared to other countries. Using Enterprise Survey data, Barbosa
Filho and Correa (2017) show that the distribution of Brazilian companies has more "heavy" tails
0
1
2
3
4
5
6
Brazil(2005)
US (1997) Argentina(2002)
Chile(2006)
Mexico(2004)
Colombia(1998)
TFPQ dispersion
90th - 10 th 75th - 25th SD
25
than other countries, especially in low productivity companies, but also in those with higher
productivity, as shown by the peak in the upper tail of the sample density function (Figure 32).
That is, the other countries have a more concentrated distribution of companies around average
productivity. This fact seems to indicate that Brazil concentrates companies of low productivity in
relation to similar countries. In addition, the dispersion of productivity presented in Brazil is the
largest among the countries analyzed. The high dispersion observed in Brazi, associated with the
concentration of companies with low productivity reduces the average productivity of the
economy. The existence of some level of dispersion (heterogeneity) among firms is not only
expected, but the result of mechanisms associated with the differentiation of product, market
(geographic) and the selection process, in which companies evolve over time in a different way.
However, the dispersions shown in Figure 30 are very large, indicating the existence of
mechanisms which undermine competition and thereby enable low productivity firms to remain
active, at the expense of higher productivity firms. Disaggregating the data at the sectoral level
shows that the problem affects most sectors of the economy, albeit to different extents (Figure 32).
Figure 32.a: Distribution of LN of labor productivity
Figure 32.b: Distribution of LN of labor productivity in the textile sector
Figure 32.c: Distribution of LN of labor productivity in the clothing sector
Figure 32.d: Distribution of the LN of labor productivity in the machinery and metallurgy sector
Figure 32.e: Distribution of LN of labor productivity in the commerce sector
Figure 32.f: Distribution of the LN of labor productivity in the hotel and restaurant sector
26
Source: Barbosa Filho and Correa (2017), based on WBES data.
35. The source of misallocation comes from within sector. An analysis of the sources of
misallocation at the firm level highlights that misallocation originates largely within-sector. Figure
33 shows the difference between the aggregate manufacturing product and the product implied by
the efficient allocation using US industry shares and Brazil firm shares. When using US industry
shares, it assumes that all firms within-sector have the same factor intensities of US industries and
that any differences in factor expenditures reflect distortions. The line labeled “within-sector only”
shows actual output divided the output if resources were allocated optimally within sector. The
line labeled “between-sector only” shows the output ratio that could be attained if resources were
allocated optimally across sector. And the line labeled “both” shows actual output divided by the
output that could be attained if resources were allocated optimally across all firms. The dominant
source of misallocation originates from within-sector as the ratio is lower than between-sector’s.
In addition, the within-sector effect varies more over time than the between sector effect. In 2005,
the year of lowest misallocation, the product is 44.7 percent farther from the potential output when
using US industry share, and 54.3 percent when using Brazilian firms’ shares. This implies that an
efficient allocation of inputs will double the aggregate output in 2005.
27
Figure 33. Misallocation reflects the dynamic effect of the within-industry allocation
Source: Vasconc elos (2015)
Note: Grey lines indicate three shocks which occurred during the period: the Asian financial crisis reduced the availability of capital in the Brazilian economy, which had a recent stabilization of inflation and a regime of fixed exchange rates; the second crisis was a domestic crisis in 2002 caused by uncertainties in the Brazilian political situation; the third was the sub-prime crisis of 2008-2009.
Figure 34. Factor misallocation has been increasing over time
Figure 35. Capital misallocation substantially larger than labor misallocation
Source: Vasconcelos (2017) Source: Busso, Madrigal and Pages (2013)
36. Misallocation has been increasing over time with capital misallocation substantially
larger than labor misallocation. The overall misallocation of factors of production in
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
1995 2000 2005 2010 2015
Factor misallocation in manufacturing(2000-2011)
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
2000 2002 2004 2006 2008 2010
Misallocation in manufacturing: labor vs capital
(2000-2011)
labor capital
28
manufacturing has increased greatly from 2000 to 2011 (Figure 34). 11 Furthermore, when
decomposing misallocation into capital and labor misallocation, it shows that capital misallocation
is three to five times larger than labor misallocation (Figure 35).12 While the gap has been closing
slowly since 2000, it remains substantial. The results is consistent with earlier findings. Figure 36
shows the dynamics of input wedges between 90th and 10th percentiles and between 75th and 25th
percentiles of the respective distributions among firms for capital and for labor using US and
Brazilian firm-specific shares . The dispersion, measured as ratios of 90th and 10th percentiles for
example, is larger for capital than for labor, suggesting capital misallocation is larger in the
Brazilian manufacturing sector. Although when using Brazilian firm specific shares, the difference
between capital and labor dispersions is much smaller, the capital wedge is still larger than labor’s.
In line with the discussion in Section II (and Chapter 3), this pattern could be the result of policy
distortions in the financial markets.
Figure 36. Productivity wedge between 90th and 10th percentile firm is substantially larger for capital than for labor
Source: Vasconcelos (2017)
Note: Grey lines indicate three shocks which occurred during the period: the Asian financial crisis reduced the availability of capital in the Brazilian economy, which had a recent stabilization of inflation and a regime of fixed exchange rates; the second crisis was a domestic crisis in 2002 caused by uncertainties in the Brazilian political situation; the third was the sub-prime crisis of 2008-2009.
37. Overall the analysis of firm dynamics signals the existence of significant market
distortions. The productivity distribution of Brazilian firms is characterized by large dispersion,
with an unusually large lower tail—indicating that unproductive firms are able to survive in the
market, presumably thanks to policies which support them. Within-firm productivity growth
dominates over between-firm productivity dynamics, suggesting room for productivity growth
11 The misallocation measure is the allocative component of the Olley Pakes (1996) decomposition. That is the
difference in the output-weighted mean productivity (TFPF) and the average TFPR. When it is negative it means
that technical efficiency (simple mean) is higher than aggregate productivity (weighted mean). 12 Following Duranton et. al (2015) method for the decomposition.
29
through factor reallocation. There is also evidence of large within-firm capital (or credit)
misallocation.
IV. Concluding Remarks
38. This chapter highlights that the Brazilian economy is characterized by significant
distortions which hamper productivity growth. At the aggregate level, it is clear that Brazil’s
growth over the past two decades has been led by factor accumulation, notably labor, while
productivity growth has been very weak. Structural shifts in employment from agriculture to
services, which is a more productive sector, have positively contributed to overall labor
productivity growth. This has been accompanied by a significant expansion in services which
absorbed large part of the demographic increase and increased levels of employment and
participation. The contribution of capital, as reflected in levels of investment, has also been limited.
39. Most of the economy appears to be paralyzed, in stagnant productivity. In terms of
sectoral performance, agriculture productivity growth has been highest, but its level is still lower
than other sectors, and given its relatively small share in the economy, its impact in overall
productivity will be limited. Within the services sector, there has been a significant productivity
growth in business services (notably financial services) and transport and telecommunications.
There is a question as to whether the expansion is these sectors does not in fact reflect rents
extraction resulting from limited competition. Nevertheless, what is also noteworthy is that the
rest of the economy appears to be paralyzed, in stagnant productivity. Since the demographic boom
is near exhausted then there is not likely to be space to continue this pattern of growth. Further,
looking forward, it seems reasonable to expect slower sectoral reallocation (of labor from
agriculture to services/manufacturing) given that the employment share in agriculture almost
halved between 1996 and 2014. Hence it is urgent for Brazil to tackle the barriers to faster
productivity growth.
40. Firm-level analysis confirms the productivity problem, and highlights the existence
of significantdistortions that hamper the operation of Schumpeterian creative destruction.
Productivity distribution across firms is characterized by large TFP dispersion, especially among
less productive firms, suggesting a lack of creative destruction. Within-firm productivity growth
dominates over between-firm productivity dynamics, suggesting room for productivity growth
through factor reallocation. And there is large within-sector TFP dispersion despite relatively little
evidence labor misallocation, suggesting large capital (or credit) misallocation.
41. The macro- and micro-level analyses suggest that low productivity growth results
mainly from barriers to competition, including a highly-distorted policy environment and
limited trade integration, as well as financial market distortions. The findings of the analysis
are consistent with the characteristics of Brazil’s policy environment which undermine
competition, instead of fostering it—thereby undermining the growth of more productive firms.
The findings also seems to be associated with existing distortions in financial markets—which
limit the level of investment and also distort the allocation to suboptimal projects, and possibly
result in abnormal profits for the financial sector—and also by an environment which does not
favor competition, resulting in the survival of low productivity firms at the expense of the growth
of more productive ones. In addition, managerial quality, incentives to innovate, low quality of
education, labor market programs incentivizing high job turnover, and an environment not
30
conducive to innovation and connectivity with the global economy all have played a role to further
reduce productivity. The persistence of poorly-run firms is evidence of insufficient market
competition, insufficient management training (education shortfalls), and inappropriate ownership
structures (family-owned and managed firms are more poorly managed than family firms with an
external CEO). All of these areas are susceptible to horizontal neutral policies across firms, in
contrast to the prevalent non-neutral industrial policies utilized in Brazil. In the following chapters,
specific productivity drivers – and associated policy levers – will be examined in greater detail.
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